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Stockholm University Press, Tellus A: Dynamic Meteorology and Oceanography, 2009

DOI: 10.3402/tellusa.v61i2.15543

Stockholm University Press, Tellus A: Dynamic Meteorology and Oceanography, 2(61), p. 210, 2009

DOI: 10.1111/j.1600-0870.2008.00378.x

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Observation bias correction with an ensemble Kalman filter

Journal article published in 2009 by Elana J. Fertig ORCID, Seung-Jong Baek, Brian R. Hunt, Edward Ott, Istvan Szunyogh, José A Aravéquia, José A. Aravéquia, Eugenia Kalnay, Hong Li, Junjie Liu
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme—the local ensemble transform Kalman filter (LETKF)—to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels on the simplified parameterizations, primitive-equation dynamics (SPEEDY) model. The scheme we present successfully reduces both the observation bias and analysis error in perfect-model simulations.